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Table of Contents
Intro; Preface; Audience and Goal of This Book; Acknowledgements; Contents; Chapter 1: Introduction; 1.1 Why Prediction Is Required for Industrial Process; 1.2 Category of Data-Based Industrial Process Prediction; 1.2.1 Data Feature-Based Prediction; 1.2.2 Time Scale-Based Prediction; 1.2.3 Prediction Reliability-Based Prediction; 1.3 Commonly Used Techniques for Industrial Prediction; 1.3.1 Time Series Prediction Methods; 1.3.2 Factor-Based Prediction Methods; 1.3.3 Methods for PIs Construction; 1.3.4 Long-Term Prediction Intervals Methods; 1.4 Summary; References.
Chapter 2: Data Preprocessing Techniques2.1 Introduction; 2.2 Anomaly Data Detection; 2.2.1 K-Nearest-Neighbor; 2.2.2 Fuzzy C Means; 2.2.3 Adaptive Fuzzy C Means; 2.2.4 Trend Anomaly Detection Based on AFCM and DTW; 2.2.5 Deviants Detection Based on KNN-AFCM; 2.2.6 Case Study; 2.3 Data Imputation; 2.3.1 Data-Missing Mechanism; 2.3.2 Regression Filling Method; 2.3.3 Expectation Maximum; 2.3.4 Varied Window Similarity Measure; 2.3.5 Segmented Shape-Representation Based Method; Key-Sliding-Window for Sequence Segmentation; Representation of Sequence Segmentation.
Procedure of Data Imputation Based on Segmented Shape-Representation2.3.6 Non-equal-Length Granules Correlation; Calculation for NGCC; NGCC-Based Correlation Analysis; Correlation-Based Data Imputation; 2.3.7 Case Study; 2.4 Data De-noising Techniques; 2.4.1 Empirical Mode Decomposition; 2.4.2 Case Study; 2.5 Discussion; References; Chapter 3: Industrial Time Series Prediction; 3.1 Introduction; 3.2 Phase Space Reconstruction; 3.2.1 Determination of Embedding Dimensionality; False Nearest-Neighbor Method (FNN); Cao Method; 3.2.2 Determination of Delay Time; Autocorrelation Function Method.
Mutual Information Method3.2.3 Simultaneous Determination of Embedding Dimensionality and Delay Time; 3.3 Linear Models for Regression; 3.3.1 Basic Linear Regression; 3.3.2 Probabilistic Linear Regression; 3.4 Gaussian Process-Based Prediction; 3.4.1 Kernel-Based Regression; 3.4.2 Gaussian Process for Prediction; 3.4.3 Gaussian Process-Based ESN; 3.4.4 Case Study; 3.5 Artificial Neural Networks-Based Prediction; 3.5.1 RNNs for Regression; 3.5.2 ESN for Regression; 3.5.3 SVD-Based ESN for Industrial Prediction; 3.5.4 ESNs with Leaky Integrator Neurons; 3.5.5 Dual Estimation-Based ESN.
3.5.6 Case StudyExtended Kalman-Filter-Based Elman Network; SVD-Based ESN for Industrial Prediction; ESN with Leaky Integrator Neurons; Dual Estimation-Based ESN; 3.6 Support Vector Machine-Based Prediction; 3.6.1 Basic Concept of SVM; 3.6.2 SVMs for Regression; 3.6.3 Least Square Support Vector Machine; 3.6.4 Sample Selection-Based Reduced SVM; 3.6.5 Bayesian Treatment for LSSVM Regression; Probabilistic Interpretation of LSSVM Regressor (Level 1): Predictive Mean and Error Bars; Calculation of Maximum Posterior; Moderated Output of LSSVM Regressor; Inference of Hyper-Parameters (Level 2).
Chapter 2: Data Preprocessing Techniques2.1 Introduction; 2.2 Anomaly Data Detection; 2.2.1 K-Nearest-Neighbor; 2.2.2 Fuzzy C Means; 2.2.3 Adaptive Fuzzy C Means; 2.2.4 Trend Anomaly Detection Based on AFCM and DTW; 2.2.5 Deviants Detection Based on KNN-AFCM; 2.2.6 Case Study; 2.3 Data Imputation; 2.3.1 Data-Missing Mechanism; 2.3.2 Regression Filling Method; 2.3.3 Expectation Maximum; 2.3.4 Varied Window Similarity Measure; 2.3.5 Segmented Shape-Representation Based Method; Key-Sliding-Window for Sequence Segmentation; Representation of Sequence Segmentation.
Procedure of Data Imputation Based on Segmented Shape-Representation2.3.6 Non-equal-Length Granules Correlation; Calculation for NGCC; NGCC-Based Correlation Analysis; Correlation-Based Data Imputation; 2.3.7 Case Study; 2.4 Data De-noising Techniques; 2.4.1 Empirical Mode Decomposition; 2.4.2 Case Study; 2.5 Discussion; References; Chapter 3: Industrial Time Series Prediction; 3.1 Introduction; 3.2 Phase Space Reconstruction; 3.2.1 Determination of Embedding Dimensionality; False Nearest-Neighbor Method (FNN); Cao Method; 3.2.2 Determination of Delay Time; Autocorrelation Function Method.
Mutual Information Method3.2.3 Simultaneous Determination of Embedding Dimensionality and Delay Time; 3.3 Linear Models for Regression; 3.3.1 Basic Linear Regression; 3.3.2 Probabilistic Linear Regression; 3.4 Gaussian Process-Based Prediction; 3.4.1 Kernel-Based Regression; 3.4.2 Gaussian Process for Prediction; 3.4.3 Gaussian Process-Based ESN; 3.4.4 Case Study; 3.5 Artificial Neural Networks-Based Prediction; 3.5.1 RNNs for Regression; 3.5.2 ESN for Regression; 3.5.3 SVD-Based ESN for Industrial Prediction; 3.5.4 ESNs with Leaky Integrator Neurons; 3.5.5 Dual Estimation-Based ESN.
3.5.6 Case StudyExtended Kalman-Filter-Based Elman Network; SVD-Based ESN for Industrial Prediction; ESN with Leaky Integrator Neurons; Dual Estimation-Based ESN; 3.6 Support Vector Machine-Based Prediction; 3.6.1 Basic Concept of SVM; 3.6.2 SVMs for Regression; 3.6.3 Least Square Support Vector Machine; 3.6.4 Sample Selection-Based Reduced SVM; 3.6.5 Bayesian Treatment for LSSVM Regression; Probabilistic Interpretation of LSSVM Regressor (Level 1): Predictive Mean and Error Bars; Calculation of Maximum Posterior; Moderated Output of LSSVM Regressor; Inference of Hyper-Parameters (Level 2).